Rivet analyses
Studies of the underlying event at 7 TeV with track-jets
Experiment: ATLAS (LHC)
Inspire ID: 1125575
Status: VALIDATED
Authors: - Kiran Joshi
References: - Expt page: ATLAS-STDM-2011-30 - arXiv: 1208.0563
Beams: p+ p+
Beam energies: (3500.0, 3500.0)GeV
Run details: - Minimum bias events at 7 TeV.
Distributions sensitive to the underlying event are studied in events containing one or more charged particles. Jets are reconstructed using the anti-kt algorithm with radius parameter R varying between 0.2 and 1.0. Distributions of the charged-particle multiplicity, the scalar sum of the transverse momentum of charged particles, and the average charged-particle pT are measured as functions of $\pT^\text{jet}$ in regions transverse to and opposite the leading jet for $4 \GeV < \pT^\text{jet} < 100 \GeV$. In addition, the R-dependence of the mean values of these observables is studied.
Source
code:ATLAS_2012_I1125575.cc
// -*- C++ -*-
#include "Rivet/Analysis.hh"
#include "Rivet/Projections/ChargedFinalState.hh"
#include "Rivet/Projections/FastJets.hh"
namespace Rivet {
/// ATLAS charged particle jet underlying event and jet radius dependence
class ATLAS_2012_I1125575 : public Analysis {
public:
/// @name Constructors etc.
/// @{
/// Constructor
RIVET_DEFAULT_ANALYSIS_CTOR(ATLAS_2012_I1125575);
/// @}
/// @name Analysis methods
/// @{
/// Book histograms and initialise projections before the run
void init() {
const ChargedFinalState jet_input((Cuts::etaIn(-2.5, 2.5) && Cuts::pT >= 0.5*GeV));
declare(jet_input, "JET_INPUT");
const ChargedFinalState track_input((Cuts::etaIn(-1.5, 1.5) && Cuts::pT >= 0.5*GeV));
declare(track_input, "TRACK_INPUT");
const FastJets jets02(jet_input, JetAlg::ANTIKT, 0.2);
declare(jets02, "JETS_02");
const FastJets jets04(jet_input, JetAlg::ANTIKT, 0.4);
declare(jets04, "JETS_04");
const FastJets jets06(jet_input, JetAlg::ANTIKT, 0.6);
declare(jets06, "JETS_06");
const FastJets jets08(jet_input, JetAlg::ANTIKT, 0.8);
declare(jets08, "JETS_08");
const FastJets jets10(jet_input, JetAlg::ANTIKT, 1.0);
declare(jets10, "JETS_10");
// Mean number of tracks
initializeProfiles(_h_meanNch, 1);
// Mean of the average track pT in each region
initializeProfiles(_h_meanPtAvg, 2);
// Mean of the scalar sum of track pT in each region
initializeProfiles(_h_meanPtSum, 3);
// Distribution of Nch, in bins of leading track-jet pT
initializeHistograms(_h_Nch, 4);
// Distribution of average track-jet pT, in bins of leading track-jet pT
initializeHistograms(_h_PtAvg, 5);
// Distribution of sum of track-jet pT, in bins of leading track-jet pT
initializeHistograms(_h_PtSum, 6);
for (int i = 0; i < 5; ++i)
book(_nEvents[i], "nEvents_"+to_str(i));
}
void initializeProfiles(Profile1DPtr plots[5][2], int distribution) {
for (int i = 0; i < 5; ++i) {
for (int j = 0; j < 2; ++j) {
book(plots[i][j], distribution, i+1, j+1);
}
}
}
void initializeHistograms(Histo1DGroupPtr plots[5][2], int distribution) {
const Estimate1D& refest = refData(1, 1, 1);
for (size_t i = 0; i < 5; ++i) {
for (size_t y = 0; y < 2; ++y) {
book(plots[i][y], refest.xEdges());
for (auto& b : plots[i][y]->bins()) {
size_t histogram_number = (b.index()*2)-((y+1)%2);
book(b, distribution, i+1, histogram_number);
}
}
}
}
/// Perform the per-event analysis
void analyze(const Event& event) {
vector<Jets*> all_jets;
Jets jets_02 = apply<FastJets>(event, "JETS_02").jetsByPt(Cuts::pT > 4*GeV && Cuts::abseta < 1.5);
all_jets.push_back(&jets_02);
Jets jets_04 = apply<FastJets>(event, "JETS_04").jetsByPt(Cuts::pT > 4*GeV && Cuts::abseta < 1.5);
all_jets.push_back(&jets_04);
Jets jets_06 = apply<FastJets>(event, "JETS_06").jetsByPt(Cuts::pT > 4*GeV && Cuts::abseta < 1.5);
all_jets.push_back(&jets_06);
Jets jets_08 = apply<FastJets>(event, "JETS_08").jetsByPt(Cuts::pT > 4*GeV && Cuts::abseta < 1.5);
all_jets.push_back(&jets_08);
Jets jets_10 = apply<FastJets>(event, "JETS_10").jetsByPt(Cuts::pT > 4*GeV && Cuts::abseta < 1.5);
all_jets.push_back(&jets_10);
// Count the number of tracks in the away and transverse regions, for each set of jets
double n_ch[5][2] = { {0,0}, {0,0}, {0,0}, {0,0}, {0,0} };
// Also add up the sum pT
double sumpt[5][2] = { {0,0}, {0,0}, {0,0}, {0,0}, {0,0} };
// ptmean = sumpt / n_ch
double ptavg[5][2] = { {0,0}, {0,0}, {0,0}, {0,0}, {0,0} };
// lead jet pT defines which bin we want to fill
double lead_jet_pts[5] = {0.0};
// Loop over each of the jet radii:
for (int i = 0; i < 5; ++i) {
if (all_jets[i]->size() < 1) continue;
// Find the lead jet pT
lead_jet_pts[i] = all_jets[i]->at(0).pT();
// Loop over each of the charged particles
const Particles& tracks = apply<ChargedFinalState>(event, "TRACK_INPUT").particlesByPt();
for(const Particle& t : tracks) {
// Get the delta-phi between the track and the leading jet
double dphi = deltaPhi(all_jets[i]->at(0), t);
// Find out which region this puts it in.
// 0 = away region, 1 = transverse region, 2 = toward region
int region = region_index(dphi);
// If the track is in the toward region, ignore it.
if (region == 2) continue;
// Otherwise, increment the relevant counters
++n_ch[i][region];
sumpt[i][region] += t.pT();
}
// Calculate the pT_avg for the away and transverse regions.
// (And make sure we don't try to divide by zero.)
ptavg[i][0] = (n_ch[i][0] == 0 ? 0.0 : sumpt[i][0] / n_ch[i][0]);
ptavg[i][1] = (n_ch[i][1] == 0 ? 0.0 : sumpt[i][1] / n_ch[i][1]);
_nEvents[i]->fill();
}
fillProfiles(_h_meanNch, n_ch, lead_jet_pts, 1.0 / (2*PI));
fillProfiles(_h_meanPtAvg, ptavg, lead_jet_pts, 1.0);
fillProfiles(_h_meanPtSum, sumpt, lead_jet_pts, 1.0 / (2*PI));
fillHistograms(_h_Nch, n_ch, lead_jet_pts);
fillHistograms(_h_PtAvg, ptavg, lead_jet_pts);
fillHistograms(_h_PtSum, sumpt, lead_jet_pts);
}
void fillProfiles(Profile1DPtr plots[5][2], double var[5][2], double lead_pt[5], double scale) {
for (int i=0; i<5; ++i) {
double pt = lead_pt[i];
for (int j=0; j<2; ++j) {
double v = var[i][j];
plots[i][j]->fill(pt, v*scale);
}
}
}
void fillHistograms(Histo1DGroupPtr plots[5][2], double var[5][2], double lead_pt[5]) {
for (int i=0; i<5; ++i) {
double pt = lead_pt[i];
for (int j=0; j<2; ++j) {
double v = var[i][j];
plots[i][j]->fill(pt, v);
}
}
}
int region_index(double dphi) {
assert(inRange(dphi, 0.0, PI, CLOSED, CLOSED));
if (dphi < PI/3.0) return 2;
if (dphi < 2*PI/3.0) return 1;
return 0;
}
/// Normalise histograms etc., after the run
void finalize() {
for (size_t i = 0; i < 5; ++i) {
scale(_h_Nch[i], 1.0/ *_nEvents[i]);
scale(_h_PtAvg[i], 1.0/ *_nEvents[i]);
scale(_h_PtSum[i], 1.0/ *_nEvents[i]);
}
}
/// @}
private:
// Data members like post-cuts event weight counters go here
CounterPtr _nEvents[5];
Profile1DPtr _h_meanNch[5][2];
Profile1DPtr _h_meanPtAvg[5][2];
Profile1DPtr _h_meanPtSum[5][2];
Histo1DGroupPtr _h_Nch[5][2];
Histo1DGroupPtr _h_PtAvg[5][2];
Histo1DGroupPtr _h_PtSum[5][2];
};
RIVET_DECLARE_PLUGIN(ATLAS_2012_I1125575);
}